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What do VM0047 Control Plots Look Like? 

In our previous article, we talked about control plots being the more robust way of estimating baselines in ARR projects. So, VM0047 control plots are critical, but how is it selected and what do some of those numbers look like?


For ease of understanding, we’ve created a simple illustration for you (note: in this case, we have used NDVI as a component of the stocking index, but it may not be the best for all use cases, more on that later).


As you can see in the table below, the matched control plots are statistically similar to the sample plots. Though more remote sensing components ought to be considered, this suggests the control plots may be good choices as a potential proxy for your project area based on this parameter as it is statistically similar to the project sample plots.


Control plots
Image credit: https://mygeoblog.com/2019/04/29/cambodia-1-mapping-vegetation-using-landsat/

Evidently, the ability to track all these control plots (there can be quite a handful) accurately over time will be critical. GIS code-based automations are likely the best way forward.


So, what does this mean for your project? 


  • Improved Accuracy: Using control plots that are statistically similar to your sample plots enhances the accuracy of carbon baseline estimates.

  • Better Insights: You gain a clearer understanding of your project’s impact on carbon accounting.


 

Reach out if you require carbon due diligence services or looking for a Technical Partner on your projects.

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